Overview

Dataset statistics

Number of variables16
Number of observations421570
Missing cells1422431
Missing cells (%)21.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory51.9 MiB
Average record size in memory129.0 B

Variable types

Numeric13
Categorical2
Boolean1

Warnings

Date has a high cardinality: 143 distinct values High cardinality
MarkDown1 is highly correlated with MarkDown4High correlation
MarkDown4 is highly correlated with MarkDown1High correlation
Size is highly correlated with MarkDown5High correlation
MarkDown1 is highly correlated with MarkDown4 and 1 other fieldsHigh correlation
MarkDown4 is highly correlated with MarkDown1High correlation
MarkDown5 is highly correlated with Size and 1 other fieldsHigh correlation
MarkDown1 is highly correlated with MarkDown4High correlation
MarkDown4 is highly correlated with MarkDown1High correlation
Unemployment is highly correlated with Fuel_Price and 2 other fieldsHigh correlation
Fuel_Price is highly correlated with Unemployment and 1 other fieldsHigh correlation
Type is highly correlated with Store and 1 other fieldsHigh correlation
MarkDown1 is highly correlated with MarkDown4High correlation
Store is highly correlated with Unemployment and 3 other fieldsHigh correlation
MarkDown4 is highly correlated with MarkDown1High correlation
Temperature is highly correlated with Fuel_PriceHigh correlation
CPI is highly correlated with Unemployment and 2 other fieldsHigh correlation
Size is highly correlated with Type and 2 other fieldsHigh correlation
MarkDown1 has 270889 (64.3%) missing values Missing
MarkDown2 has 310322 (73.6%) missing values Missing
MarkDown3 has 284479 (67.5%) missing values Missing
MarkDown4 has 286603 (68.0%) missing values Missing
MarkDown5 has 270138 (64.1%) missing values Missing
Date is uniformly distributed Uniform

Reproduction

Analysis started2021-11-13 19:13:50.983349
Analysis finished2021-11-13 19:14:44.353926
Duration53.37 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Store
Real number (ℝ≥0)

HIGH CORRELATION

Distinct45
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.20054558
Minimum1
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-11-14T00:44:44.447676image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median22
Q333
95-th percentile43
Maximum45
Range44
Interquartile range (IQR)22

Descriptive statistics

Standard deviation12.78529739
Coefficient of variation (CV)0.5759001437
Kurtosis-1.146502781
Mean22.20054558
Median Absolute Deviation (MAD)11
Skewness0.07776250175
Sum9359084
Variance163.4638293
MonotonicityIncreasing
2021-11-14T00:44:44.569349image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1310474
 
2.5%
1010315
 
2.4%
410272
 
2.4%
110244
 
2.4%
210238
 
2.4%
2410228
 
2.4%
2710225
 
2.4%
3410224
 
2.4%
2010214
 
2.4%
610211
 
2.4%
Other values (35)318925
75.7%
ValueCountFrequency (%)
110244
2.4%
210238
2.4%
39036
2.1%
410272
2.4%
58999
2.1%
610211
2.4%
79762
2.3%
89895
2.3%
98867
2.1%
1010315
2.4%
ValueCountFrequency (%)
459637
2.3%
447169
1.7%
436751
1.6%
426953
1.6%
4110088
2.4%
4010017
2.4%
399878
2.3%
387362
1.7%
377206
1.7%
366222
1.5%

Dept
Real number (ℝ≥0)

Distinct81
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.26031739
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-11-14T00:44:44.688064image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q118
median37
Q374
95-th percentile95
Maximum99
Range98
Interquartile range (IQR)56

Descriptive statistics

Standard deviation30.49205402
Coefficient of variation (CV)0.6889253358
Kurtosis-1.215570579
Mean44.26031739
Median Absolute Deviation (MAD)23
Skewness0.3582231935
Sum18658822
Variance929.7653581
MonotonicityNot monotonic
2021-11-14T00:44:44.811699image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16435
 
1.5%
136435
 
1.5%
26435
 
1.5%
466435
 
1.5%
676435
 
1.5%
796435
 
1.5%
216435
 
1.5%
816435
 
1.5%
826435
 
1.5%
906435
 
1.5%
Other values (71)357220
84.7%
ValueCountFrequency (%)
16435
1.5%
26435
1.5%
36435
1.5%
46435
1.5%
56347
1.5%
65986
1.4%
76435
1.5%
86435
1.5%
96354
1.5%
106435
1.5%
ValueCountFrequency (%)
99862
 
0.2%
985836
1.4%
976278
1.5%
964854
1.2%
956435
1.5%
945685
1.3%
935913
1.4%
926435
1.5%
916435
1.5%
906435
1.5%

Date
Categorical

HIGH CARDINALITY
UNIFORM

Distinct143
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.4 MiB
2011-12-23
 
3027
2011-11-25
 
3021
2011-12-16
 
3013
2011-12-09
 
3010
2012-02-17
 
3007
Other values (138)
406492 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters4215700
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2010-02-05
2nd row2010-02-05
3rd row2010-02-05
4th row2010-02-05
5th row2010-02-05

Common Values

ValueCountFrequency (%)
2011-12-233027
 
0.7%
2011-11-253021
 
0.7%
2011-12-163013
 
0.7%
2011-12-093010
 
0.7%
2012-02-173007
 
0.7%
2011-12-303003
 
0.7%
2012-02-103001
 
0.7%
2011-12-022994
 
0.7%
2012-03-022990
 
0.7%
2012-10-122990
 
0.7%
Other values (133)391514
92.9%

Length

2021-11-14T00:44:45.053165image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2011-12-233027
 
0.7%
2011-11-253021
 
0.7%
2011-12-163013
 
0.7%
2011-12-093010
 
0.7%
2012-02-173007
 
0.7%
2011-12-303003
 
0.7%
2012-02-103001
 
0.7%
2011-12-022994
 
0.7%
2012-03-022990
 
0.7%
2012-10-122990
 
0.7%
Other values (133)391514
92.9%

Most occurring characters

ValueCountFrequency (%)
01098526
26.1%
1899707
21.3%
-843140
20.0%
2791099
18.8%
3103408
 
2.5%
482539
 
2.0%
682450
 
2.0%
782241
 
2.0%
979610
 
1.9%
576564
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3372560
80.0%
Dash Punctuation843140
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01098526
32.6%
1899707
26.7%
2791099
23.5%
3103408
 
3.1%
482539
 
2.4%
682450
 
2.4%
782241
 
2.4%
979610
 
2.4%
576564
 
2.3%
876416
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
-843140
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4215700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01098526
26.1%
1899707
21.3%
-843140
20.0%
2791099
18.8%
3103408
 
2.5%
482539
 
2.0%
682450
 
2.0%
782241
 
2.0%
979610
 
1.9%
576564
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII4215700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01098526
26.1%
1899707
21.3%
-843140
20.0%
2791099
18.8%
3103408
 
2.5%
482539
 
2.0%
682450
 
2.0%
782241
 
2.0%
979610
 
1.9%
576564
 
1.8%

Weekly_Sales
Real number (ℝ)

Distinct359464
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15981.25812
Minimum-4988.94
Maximum693099.36
Zeros73
Zeros (%)< 0.1%
Negative1285
Negative (%)0.3%
Memory size6.4 MiB
2021-11-14T00:44:45.153358image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-4988.94
5-th percentile59.9745
Q12079.65
median7612.03
Q320205.8525
95-th percentile61201.951
Maximum693099.36
Range698088.3
Interquartile range (IQR)18126.2025

Descriptive statistics

Standard deviation22711.18352
Coefficient of variation (CV)1.421113616
Kurtosis21.49128991
Mean15981.25812
Median Absolute Deviation (MAD)6747.645
Skewness3.262008185
Sum6737218987
Variance515797856.8
MonotonicityNot monotonic
2021-11-14T00:44:45.263072image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10353
 
0.1%
5289
 
0.1%
20232
 
0.1%
15215
 
0.1%
12175
 
< 0.1%
1169
 
< 0.1%
10.47167
 
< 0.1%
11.97154
 
< 0.1%
2148
 
< 0.1%
7146
 
< 0.1%
Other values (359454)419522
99.5%
ValueCountFrequency (%)
-4988.941
 
< 0.1%
-39241
 
< 0.1%
-17501
 
< 0.1%
-16991
 
< 0.1%
-1321.481
 
< 0.1%
-10983
< 0.1%
-1008.961
 
< 0.1%
-8981
 
< 0.1%
-8631
 
< 0.1%
-7984
< 0.1%
ValueCountFrequency (%)
693099.361
< 0.1%
649770.181
< 0.1%
630999.191
< 0.1%
627962.931
< 0.1%
474330.11
< 0.1%
422306.251
< 0.1%
420586.571
< 0.1%
406988.631
< 0.1%
404245.031
< 0.1%
393705.21
< 0.1%

IsHoliday
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
False
391909 
True
 
29661
ValueCountFrequency (%)
False391909
93.0%
True29661
 
7.0%
2021-11-14T00:44:45.339867image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Type
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.4 MiB
A
215478 
B
163495 
C
42597 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters421570
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A215478
51.1%
B163495
38.8%
C42597
 
10.1%

Length

2021-11-14T00:44:45.498934image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-14T00:44:45.738289image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
a215478
51.1%
b163495
38.8%
c42597
 
10.1%

Most occurring characters

ValueCountFrequency (%)
A215478
51.1%
B163495
38.8%
C42597
 
10.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter421570
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A215478
51.1%
B163495
38.8%
C42597
 
10.1%

Most occurring scripts

ValueCountFrequency (%)
Latin421570
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A215478
51.1%
B163495
38.8%
C42597
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII421570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A215478
51.1%
B163495
38.8%
C42597
 
10.1%

Size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136727.9157
Minimum34875
Maximum219622
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-11-14T00:44:45.812083image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum34875
5-th percentile39690
Q193638
median140167
Q3202505
95-th percentile206302
Maximum219622
Range184747
Interquartile range (IQR)108867

Descriptive statistics

Standard deviation60980.58333
Coefficient of variation (CV)0.4459995093
Kurtosis-1.206345903
Mean136727.9157
Median Absolute Deviation (MAD)62140
Skewness-0.3258497665
Sum5.764038744 × 1010
Variance3718631543
MonotonicityNot monotonic
2021-11-14T00:44:45.919770image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
3969020802
 
4.9%
3991020597
 
4.9%
20381920376
 
4.8%
21962210474
 
2.5%
12651210315
 
2.4%
20586310272
 
2.4%
15131510244
 
2.4%
20230710238
 
2.4%
20418410225
 
2.4%
15811410224
 
2.4%
Other values (30)287803
68.3%
ValueCountFrequency (%)
348758999
2.1%
373929036
2.1%
3969020802
4.9%
3991020597
4.9%
410626751
 
1.6%
429887156
 
1.7%
571979443
2.2%
707139762
2.3%
931889864
2.3%
936389455
2.2%
ValueCountFrequency (%)
21962210474
2.5%
20749910062
2.4%
20630210113
2.4%
20586310272
2.4%
20418410225
2.4%
20381920376
4.8%
20375010142
2.4%
20374210214
2.4%
20300710202
2.4%
20250510211
2.4%

Temperature
Real number (ℝ)

HIGH CORRELATION

Distinct3528
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.09005873
Minimum-2.06
Maximum100.14
Zeros0
Zeros (%)0.0%
Negative69
Negative (%)< 0.1%
Memory size6.4 MiB
2021-11-14T00:44:46.039491image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-2.06
5-th percentile27.31
Q146.68
median62.09
Q374.28
95-th percentile87.27
Maximum100.14
Range102.2
Interquartile range (IQR)27.6

Descriptive statistics

Standard deviation18.44793115
Coefficient of variation (CV)0.3070047115
Kurtosis-0.6359219778
Mean60.09005873
Median Absolute Deviation (MAD)13.63
Skewness-0.321404152
Sum25332166.06
Variance340.3261636
MonotonicityNot monotonic
2021-11-14T00:44:46.158158image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.43709
 
0.2%
67.87646
 
0.2%
72.62594
 
0.1%
76.67583
 
0.1%
70.28563
 
0.1%
76.03555
 
0.1%
50.56544
 
0.1%
64.05542
 
0.1%
64.21519
 
0.1%
50.81487
 
0.1%
Other values (3518)415828
98.6%
ValueCountFrequency (%)
-2.0669
< 0.1%
5.5468
< 0.1%
6.2369
< 0.1%
7.4669
< 0.1%
9.5170
< 0.1%
9.5569
< 0.1%
10.0966
< 0.1%
10.1168
< 0.1%
10.2469
< 0.1%
10.5372
< 0.1%
ValueCountFrequency (%)
100.1444
 
< 0.1%
100.0746
 
< 0.1%
99.6648
 
< 0.1%
99.22185
< 0.1%
99.246
 
< 0.1%
98.4343
 
< 0.1%
98.1547
 
< 0.1%
97.6642
 
< 0.1%
97.648
 
< 0.1%
97.18187
< 0.1%

Fuel_Price
Real number (ℝ≥0)

HIGH CORRELATION

Distinct892
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.361026527
Minimum2.472
Maximum4.468
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-11-14T00:44:46.276815image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2.472
5-th percentile2.653
Q12.933
median3.452
Q33.738
95-th percentile4.029
Maximum4.468
Range1.996
Interquartile range (IQR)0.805

Descriptive statistics

Standard deviation0.4585145371
Coefficient of variation (CV)0.1364209813
Kurtosis-1.185404505
Mean3.361026527
Median Absolute Deviation (MAD)0.375
Skewness-0.1049014956
Sum1416907.953
Variance0.2102355808
MonotonicityNot monotonic
2021-11-14T00:44:46.400511image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.6382548
 
0.6%
3.632164
 
0.5%
2.7711917
 
0.5%
3.8911856
 
0.4%
3.5941796
 
0.4%
3.5241793
 
0.4%
3.5231792
 
0.4%
2.721790
 
0.4%
3.6661778
 
0.4%
2.781656
 
0.4%
Other values (882)402480
95.5%
ValueCountFrequency (%)
2.47238
 
< 0.1%
2.51345
 
< 0.1%
2.514906
0.2%
2.5239
 
< 0.1%
2.53342
 
< 0.1%
2.53937
 
< 0.1%
2.54147
 
< 0.1%
2.54245
 
< 0.1%
2.54538
 
< 0.1%
2.548902
0.2%
ValueCountFrequency (%)
4.468368
0.1%
4.449358
0.1%
4.308168
< 0.1%
4.301360
0.1%
4.294363
0.1%
4.293192
< 0.1%
4.288172
< 0.1%
4.282173
< 0.1%
4.277357
0.1%
4.273366
0.1%

MarkDown1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2277
Distinct (%)1.5%
Missing270889
Missing (%)64.3%
Infinite0
Infinite (%)0.0%
Mean7246.420196
Minimum0.27
Maximum88646.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-11-14T00:44:46.524817image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.27
5-th percentile149.19
Q12240.27
median5347.45
Q39210.9
95-th percentile21801.35
Maximum88646.76
Range88646.49
Interquartile range (IQR)6970.63

Descriptive statistics

Standard deviation8291.221345
Coefficient of variation (CV)1.144181695
Kurtosis17.60626321
Mean7246.420196
Median Absolute Deviation (MAD)3430.74
Skewness3.341844686
Sum1091897842
Variance68744351.4
MonotonicityNot monotonic
2021-11-14T00:44:46.633528image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
460.73102
 
< 0.1%
1.5102
 
< 0.1%
175.6493
 
< 0.1%
1483.1775
 
< 0.1%
686.2475
 
< 0.1%
9264.4875
 
< 0.1%
5924.7175
 
< 0.1%
1282.4275
 
< 0.1%
3242.5974
 
< 0.1%
1927.1574
 
< 0.1%
Other values (2267)149861
35.5%
(Missing)270889
64.3%
ValueCountFrequency (%)
0.2751
< 0.1%
0.549
< 0.1%
1.5102
< 0.1%
1.9450
< 0.1%
2.1252
< 0.1%
2.449
< 0.1%
2.4250
< 0.1%
2.4351
< 0.1%
2.850
< 0.1%
2.9151
< 0.1%
ValueCountFrequency (%)
88646.7668
< 0.1%
78124.570
< 0.1%
75149.7973
< 0.1%
65021.2373
< 0.1%
62567.666
< 0.1%
62172.7372
< 0.1%
60740.6470
< 0.1%
60394.7372
< 0.1%
58928.5272
< 0.1%
56917.771
< 0.1%

MarkDown2
Real number (ℝ)

MISSING

Distinct1499
Distinct (%)1.3%
Missing310322
Missing (%)73.6%
Infinite0
Infinite (%)0.0%
Mean3334.628621
Minimum-265.76
Maximum104519.54
Zeros207
Zeros (%)< 0.1%
Negative1311
Negative (%)0.3%
Memory size6.4 MiB
2021-11-14T00:44:46.746227image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-265.76
5-th percentile1.95
Q141.6
median192
Q31926.94
95-th percentile16497.47
Maximum104519.54
Range104785.3
Interquartile range (IQR)1885.34

Descriptive statistics

Standard deviation9475.357325
Coefficient of variation (CV)2.841503028
Kurtosis37.58956105
Mean3334.628621
Median Absolute Deviation (MAD)184.73
Skewness5.441261196
Sum370970764.8
Variance89782396.45
MonotonicityNot monotonic
2021-11-14T00:44:46.864941image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.91539
 
0.1%
3493
 
0.1%
0.5485
 
0.1%
1.5471
 
0.1%
4367
 
0.1%
6365
 
0.1%
7.64354
 
0.1%
3.82353
 
0.1%
19345
 
0.1%
5.73345
 
0.1%
Other values (1489)107131
 
25.4%
(Missing)310322
73.6%
ValueCountFrequency (%)
-265.7671
< 0.1%
-19272
< 0.1%
-2072
< 0.1%
-10.9860
< 0.1%
-10.5143
< 0.1%
-9.9868
< 0.1%
-9.9462
< 0.1%
-7.669
< 0.1%
-7.0169
< 0.1%
-6.6969
< 0.1%
ValueCountFrequency (%)
104519.5472
< 0.1%
97740.9973
< 0.1%
92523.9473
< 0.1%
89121.9474
< 0.1%
82881.1673
< 0.1%
72413.7172
< 0.1%
70574.8571
< 0.1%
58804.9169
< 0.1%
58046.4171
< 0.1%
56106.272
< 0.1%

MarkDown3
Real number (ℝ)

MISSING

Distinct1662
Distinct (%)1.2%
Missing284479
Missing (%)67.5%
Infinite0
Infinite (%)0.0%
Mean1439.421384
Minimum-29.1
Maximum141630.61
Zeros67
Zeros (%)< 0.1%
Negative257
Negative (%)0.1%
Memory size6.4 MiB
2021-11-14T00:44:46.980810image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-29.1
5-th percentile0.65
Q15.08
median24.6
Q3103.99
95-th percentile1059.9
Maximum141630.61
Range141659.71
Interquartile range (IQR)98.91

Descriptive statistics

Standard deviation9623.07829
Coefficient of variation (CV)6.685379553
Kurtosis77.68777203
Mean1439.421384
Median Absolute Deviation (MAD)22.6
Skewness8.399453018
Sum197331717
Variance92603635.78
MonotonicityNot monotonic
2021-11-14T00:44:47.094050image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3754
 
0.2%
6710
 
0.2%
2660
 
0.2%
1611
 
0.1%
0.22487
 
0.1%
0.5463
 
0.1%
0.01444
 
0.1%
4439
 
0.1%
3.2379
 
0.1%
1.98363
 
0.1%
Other values (1652)131781
31.3%
(Missing)284479
67.5%
ValueCountFrequency (%)
-29.172
 
< 0.1%
-170
 
< 0.1%
-0.8746
 
< 0.1%
-0.269
 
< 0.1%
067
 
< 0.1%
0.01444
0.1%
0.02124
 
< 0.1%
0.04241
0.1%
0.0571
 
< 0.1%
0.06205
< 0.1%
ValueCountFrequency (%)
141630.6174
< 0.1%
109030.7575
< 0.1%
103991.9472
< 0.1%
101378.7973
< 0.1%
89402.6471
< 0.1%
88805.5872
< 0.1%
83340.3374
< 0.1%
83192.8174
< 0.1%
79621.272
< 0.1%
77451.2673
< 0.1%

MarkDown4
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1944
Distinct (%)1.4%
Missing286603
Missing (%)68.0%
Infinite0
Infinite (%)0.0%
Mean3383.168256
Minimum0.22
Maximum67474.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-11-14T00:44:47.214386image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile28.76
Q1504.22
median1481.31
Q33595.04
95-th percentile12645.96
Maximum67474.85
Range67474.63
Interquartile range (IQR)3090.82

Descriptive statistics

Standard deviation6292.384031
Coefficient of variation (CV)1.859908688
Kurtosis29.99681491
Mean3383.168256
Median Absolute Deviation (MAD)1167.55
Skewness4.847500037
Sum456616070
Variance39594096.79
MonotonicityNot monotonic
2021-11-14T00:44:47.329114image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9280
 
0.1%
4200
 
< 0.1%
2197
 
< 0.1%
3146
 
< 0.1%
47143
 
< 0.1%
67.72142
 
< 0.1%
17141
 
< 0.1%
657.56141
 
< 0.1%
1330.36140
 
< 0.1%
8140
 
< 0.1%
Other values (1934)133297
31.6%
(Missing)286603
68.0%
ValueCountFrequency (%)
0.2257
 
< 0.1%
0.4152
 
< 0.1%
0.4648
 
< 0.1%
0.7852
 
< 0.1%
0.8749
 
< 0.1%
0.9245
 
< 0.1%
1.555
 
< 0.1%
1.8848
 
< 0.1%
1.9844
 
< 0.1%
2197
< 0.1%
ValueCountFrequency (%)
67474.8572
< 0.1%
57817.5674
< 0.1%
57815.4368
< 0.1%
53603.9972
< 0.1%
52739.0272
< 0.1%
48403.5370
< 0.1%
48159.8673
< 0.1%
48086.6472
< 0.1%
47452.4373
< 0.1%
46238.2871
< 0.1%

MarkDown5
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct2293
Distinct (%)1.5%
Missing270138
Missing (%)64.1%
Infinite0
Infinite (%)0.0%
Mean4628.975079
Minimum135.16
Maximum108519.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-11-14T00:44:47.448782image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum135.16
5-th percentile715.52
Q11878.44
median3359.45
Q35563.8
95-th percentile11269.24
Maximum108519.28
Range108384.12
Interquartile range (IQR)3685.36

Descriptive statistics

Standard deviation5962.887455
Coefficient of variation (CV)1.288165815
Kurtosis107.8492655
Mean4628.975079
Median Absolute Deviation (MAD)1702.47
Skewness8.169909544
Sum700974954.2
Variance35556026.8
MonotonicityNot monotonic
2021-11-14T00:44:47.564279image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2743.18136
 
< 0.1%
1064.56120
 
< 0.1%
20371.0275
 
< 0.1%
3557.6775
 
< 0.1%
3567.0375
 
< 0.1%
9083.5475
 
< 0.1%
4180.2975
 
< 0.1%
1009.9874
 
< 0.1%
7481.5874
 
< 0.1%
6016.8674
 
< 0.1%
Other values (2283)150579
35.7%
(Missing)270138
64.1%
ValueCountFrequency (%)
135.1665
< 0.1%
153.0447
< 0.1%
153.949
< 0.1%
164.0852
< 0.1%
170.6469
< 0.1%
171.7671
< 0.1%
180.0764
< 0.1%
212.7550
< 0.1%
224.8650
< 0.1%
227.1248
< 0.1%
ValueCountFrequency (%)
108519.2868
< 0.1%
105223.1170
< 0.1%
85851.8768
< 0.1%
63005.5869
< 0.1%
58068.1469
< 0.1%
57029.7868
< 0.1%
53212.7270
< 0.1%
37581.2770
< 0.1%
36430.3371
< 0.1%
36360.4272
< 0.1%

CPI
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2145
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean171.2019468
Minimum126.064
Maximum227.2328068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-11-14T00:44:47.681257image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum126.064
5-th percentile126.4962581
Q1132.0226667
median182.3187801
Q3212.4169928
95-th percentile221.9415576
Maximum227.2328068
Range101.1688068
Interquartile range (IQR)80.3943261

Descriptive statistics

Standard deviation39.15927562
Coefficient of variation (CV)0.2287314855
Kurtosis-1.829714364
Mean171.2019468
Median Absolute Deviation (MAD)41.4348629
Skewness0.08521928473
Sum72173604.72
Variance1533.448867
MonotonicityNot monotonic
2021-11-14T00:44:47.794443image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.8555333711
 
0.2%
131.1083333708
 
0.2%
129.8459667707
 
0.2%
130.3849032706
 
0.2%
131.0756667706
 
0.2%
130.6457931706
 
0.2%
130.683706
 
0.2%
130.4546207705
 
0.2%
130.7196333705
 
0.2%
131.1453333704
 
0.2%
Other values (2135)414506
98.3%
ValueCountFrequency (%)
126.064678
0.2%
126.0766452679
0.2%
126.0854516675
0.2%
126.0892903682
0.2%
126.1019355686
0.2%
126.1069032681
0.2%
126.1119032682
0.2%
126.114687
0.2%
126.1145806689
0.2%
126.1266683
0.2%
ValueCountFrequency (%)
227.232806863
< 0.1%
227.21428862
< 0.1%
227.169391963
< 0.1%
227.036935970
< 0.1%
227.018416669
< 0.1%
226.9873637134
< 0.1%
226.973544869
< 0.1%
226.9688442134
< 0.1%
226.966232563
< 0.1%
226.9239785135
< 0.1%

Unemployment
Real number (ℝ≥0)

HIGH CORRELATION

Distinct349
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.960288695
Minimum3.879
Maximum14.313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2021-11-14T00:44:47.909139image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3.879
5-th percentile5.326
Q16.891
median7.866
Q38.572
95-th percentile12.187
Maximum14.313
Range10.434
Interquartile range (IQR)1.681

Descriptive statistics

Standard deviation1.863296038
Coefficient of variation (CV)0.2340739275
Kurtosis2.73121663
Mean7.960288695
Median Absolute Deviation (MAD)0.858
Skewness1.183742568
Sum3355818.905
Variance3.471872127
MonotonicityNot monotonic
2021-11-14T00:44:48.029839image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.0995152
 
1.2%
8.1633636
 
0.9%
7.8523614
 
0.9%
7.3433416
 
0.8%
7.0573414
 
0.8%
7.9313400
 
0.8%
7.4413397
 
0.8%
6.5653370
 
0.8%
8.23361
 
0.8%
6.8913360
 
0.8%
Other values (339)385450
91.4%
ValueCountFrequency (%)
3.879287
 
0.1%
4.077938
0.2%
4.1251831
0.4%
4.145562
 
0.1%
4.1561815
0.4%
4.2611829
0.4%
4.308935
0.2%
4.421855
0.4%
4.5841988
0.5%
4.607935
0.2%
ValueCountFrequency (%)
14.3132636
0.6%
14.182423
0.6%
14.0992441
0.6%
14.0212263
0.5%
13.9751529
0.4%
13.7362464
0.6%
13.5032661
0.6%
12.892491
0.6%
12.1872507
0.6%
11.6272502
0.6%

Interactions

2021-11-14T00:44:09.759884image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:09.998006image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:10.221465image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:10.442603image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:10.676013image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:10.895890image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:11.049462image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:11.175757image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:11.318379image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:11.463022image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:11.610593image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:11.843968image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:12.106267image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:12.381571image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:12.681730image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:12.946061image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:13.189736image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:13.467008image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:13.719333image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:13.931755image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:14.092324image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:14.246880image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:14.408446image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:14.593201image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:14.946242image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:15.191626image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:15.419016image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:15.649364image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:15.872980image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:16.109294image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:16.342675image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:16.568071image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:16.732657image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:16.864870image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:17.008518image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:17.156090image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:17.308683image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:17.532085image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:17.755487image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:17.978927image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:18.203324image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:18.430724image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:18.659107image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:18.892490image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:19.135802image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:19.299398image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:19.434004image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:19.581904image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:19.734469image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:19.890079image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:20.127390image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:20.355938image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:20.581547image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:20.826891image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:21.219877image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:21.466891image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:21.697320image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:21.925695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:22.089225image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:22.216883image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:22.365486image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:22.520105image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:22.715551image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:22.950918image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:23.179286image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:23.414614image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:23.658960image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:23.898321image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:24.155513image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:24.415821image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:24.658133image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:24.856605image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:25.033135image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:25.213471image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:25.413971image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:25.613433image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:25.921343image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:26.201582image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:26.404055image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:26.583561image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:26.749291image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:26.905871image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:27.059497image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:27.198976image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:27.342560image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:27.467258image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:27.601984image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:27.742617image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:27.885267image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:28.038610image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:28.340751image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:28.576120image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:29.021929image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:29.203490image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:29.370992image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:29.553711image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:29.784083image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:29.948644image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:30.089850image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:30.225456image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:30.391045image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:30.553577image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:30.716142image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:30.869730image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:31.018517image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:31.176838image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:31.325494image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:31.478116image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:31.629170image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:31.774749image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:31.935975image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:32.087608image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:32.250172image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:32.400291image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:32.558688image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:32.716085image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:32.863347image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:33.014984image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:33.169532image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:33.326108image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:33.529602image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:33.692163image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:33.839734image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:33.992325image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:34.121978image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-11-14T00:44:34.435140image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:34.602085image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-11-14T00:44:36.943875image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-11-14T00:44:37.737959image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:37.958371image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:38.179778image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:38.386225image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:38.529170image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:38.661822image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:38.794466image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:38.933592image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:39.076987image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:39.286906image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:39.493354image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:39.712770image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:39.929569image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:40.146284image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:40.369313image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:40.589136image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:40.803316image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:40.951916image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:41.071612image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:41.203259image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:41.340299image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:41.481260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:41.692654image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-11-14T00:44:41.906554image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-11-14T00:44:48.145531image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-14T00:44:48.318069image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-14T00:44:48.489610image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-14T00:44:48.661151image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-11-14T00:44:48.809754image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-11-14T00:44:42.148903image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-14T00:44:42.745121image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-11-14T00:44:43.738423image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-11-14T00:44:43.970949image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

StoreDeptDateWeekly_SalesIsHolidayTypeSizeTemperatureFuel_PriceMarkDown1MarkDown2MarkDown3MarkDown4MarkDown5CPIUnemployment
0112010-02-0524924.50FalseA15131542.312.572NaNNaNNaNNaNNaN211.0963588.106
1122010-02-0550605.27FalseA15131542.312.572NaNNaNNaNNaNNaN211.0963588.106
2132010-02-0513740.12FalseA15131542.312.572NaNNaNNaNNaNNaN211.0963588.106
3142010-02-0539954.04FalseA15131542.312.572NaNNaNNaNNaNNaN211.0963588.106
4152010-02-0532229.38FalseA15131542.312.572NaNNaNNaNNaNNaN211.0963588.106
5162010-02-055749.03FalseA15131542.312.572NaNNaNNaNNaNNaN211.0963588.106
6172010-02-0521084.08FalseA15131542.312.572NaNNaNNaNNaNNaN211.0963588.106
7182010-02-0540129.01FalseA15131542.312.572NaNNaNNaNNaNNaN211.0963588.106
8192010-02-0516930.99FalseA15131542.312.572NaNNaNNaNNaNNaN211.0963588.106
91102010-02-0530721.50FalseA15131542.312.572NaNNaNNaNNaNNaN211.0963588.106

Last rows

StoreDeptDateWeekly_SalesIsHolidayTypeSizeTemperatureFuel_PriceMarkDown1MarkDown2MarkDown3MarkDown4MarkDown5CPIUnemployment
42156045852012-10-261689.10FalseB11822158.853.8824018.9158.08100.0211.94858.33192.3088998.667
42156145872012-10-268187.66FalseB11822158.853.8824018.9158.08100.0211.94858.33192.3088998.667
42156245902012-10-2625352.32FalseB11822158.853.8824018.9158.08100.0211.94858.33192.3088998.667
42156345912012-10-2616330.84FalseB11822158.853.8824018.9158.08100.0211.94858.33192.3088998.667
42156445922012-10-2654608.75FalseB11822158.853.8824018.9158.08100.0211.94858.33192.3088998.667
42156545932012-10-262487.80FalseB11822158.853.8824018.9158.08100.0211.94858.33192.3088998.667
42156645942012-10-265203.31FalseB11822158.853.8824018.9158.08100.0211.94858.33192.3088998.667
42156745952012-10-2656017.47FalseB11822158.853.8824018.9158.08100.0211.94858.33192.3088998.667
42156845972012-10-266817.48FalseB11822158.853.8824018.9158.08100.0211.94858.33192.3088998.667
42156945982012-10-261076.80FalseB11822158.853.8824018.9158.08100.0211.94858.33192.3088998.667